Wild animals suppress the spread of socially-transmitted misinformation

Abstract

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviours [1-3] to global misinformation epidemics [4-7]. Transmission of information through groups depends on the decision-making strategies individuals use to transform the perceived actions of others into their own behavioural actions [8-10]. Because it is often not possible to directly infer these strategies in situ, most studies of behavioural spread in groups assume individuals make decisions by pooling [7, 8, 10, 11] or averaging [8, 9] the actions or behavioural states of neighbours. Whether individuals adopt more sophisticated strategies that exploit socially-transmitted information, while remaining robust to misinformation exposure, is unknown. Here we uncover the impacts of individual decision-making on misinformation spread in natural groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously. Using automated tracking and visual field reconstruction, we infer the precise sequences of socially-transmitted stimuli perceived by each individual during decision-making. Our analysis reveals a novel feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially-transmitted cues. We find that this property can be achieved by a simple and biologically widespread decision-making circuit. This form of dynamic gain control makes individual behaviour robust to natural fluctuations in misinformation exposure, and radically alters misinformation spread relative to predictions of widely-used models of social contagion.

False alarms and information transmission in grouping animals

“…Similarly, yellow‐bellied marmots were more likely to respond to alarm calls when multiple calls were played concurrently, likely because the alarm is more likely to be accurate if it has been verified by multiple animals (Blumstein et al ., 2004). In an alternative mechanism to adjusting sensitivity based on reliability, in mixed‐species reef fish shoals, modelling suggested that fish dynamically alter their sensitivity to socially transmitted cues depending on group density to supress the transmission of false alarms (false alarms can be more common at higher densities; Fahimipour et al ., 2022).…”

Demystifying image-based machine learning: A practical guide to automated analysis of field imagery using modern machine learning tools

“…Image-based ML tools have also been used for a variety of applications beyond the three tasks described above. Examples include "structure-from-motion" studies, in which the three-dimensional structure of objects are inferred and reconstructed from a sequence of images taken from different locations in the environment (Francisco et al 2020), animal tracking and visual field reconstruction (Hein et al 2018, Fahimipour et al 2022, quantitative measurement and size estimation (Fernandes et al 2020), animal postural analysis (Graving et al 2019), and re-identification of individual animals in new images based on a set of previous observations (Nepovinnykh et al 2020).…”

“…2020), animal tracking and visual field reconstruction (Hein et al . 2018, Fahimipour et al . 2022), quantitative measurement and size estimation (Fernandes et al .…”